2020 EA survey: all responses

(
priority_ordered_bar <- lik_priority_eas20 %>% 
      as.data.frame() %>% 
    select(-engagement_num) %>% 
    likert() %>% 
  plot(.,
     type="bar") +
  ggtitle(title)
)

Figure 2.1: All responses

… ‘Highly engaged’ (self-rated)

(
priority_ordered_bar_eng <- lik_priority_eas20 %>%
  filter(engagement_num==5) %>% 
        select(-engagement_num) %>% 
   as.data.frame() %>%
    likert() %>%
  plot(type="bar") + 
  ggtitle(title)
)

Figure 2.2: Highly engaged only (rated 5/5)

How much effective altruism/global priorities research funding is there?

Open philanthropy … the largest (but not only) grantmaker

.kable_styling <- hijack(kableExtra::kable_styling, full_width=FALSE)
.kable <- hijack(knitr::kable, format.args = list(big.mark = ",", scientific = FALSE))


(
op_research_grants_tab <- 
open_phil_grants %>% 
  filter(possible_research==TRUE) %>% 
  group_by(year) %>% 
  dplyr::summarise(total =  format(sum(amount, na.rm = TRUE), big.mark=",", scientific=FALSE), grants = n()) %>%
  arrange(-year) %>% 
    mutate(year=as.character(year)) %>% 
    .kable(caption = "Open Phil (likely) research funding by year")  %>%
 .kable_styling()
)

By year and focus area:

(
  op_res_grants_tab_yr_area <-
open_phil_grants %>%
    filter(possible_research==TRUE) %>%
    dplyr::group_by(year, focus_area) %>% # drop_na(!!yvar, !!treatvar) %>%
    summarise(total = sum(amount_usd_k,  na.rm = TRUE)) %>% 
    spread(year, total, fill=0) %>%
    arrange(-`2020`) %>%
    .kable(caption = "OpenPhil (likely research) grants by year and area, in $1000 USD") %>%
    .kable_styling()
)

## `summarise()` has grouped output by 'year'. You can override using the `.groups` argument.

(
  op_res_grants_line <-
open_phil_grants %>%
  group_by(year, focus_area) %>% 
    mutate(total = sum(amount_usd_k, na.rm = TRUE)) %>%
    ggplot() +
    aes(x = year, y = amount_usd_k, colour = focus_area) +
    geom_jitter(width = 0.5, height = 0.2, size=0.8) +
  scale_colour_discrete(labels = function(x) str_wrap(x, width = 15)) +
    geom_line(aes(x=year, y=total)) +
  ylab("Grant amounts in $1k")
)

(
open_phil_grants %>% 
  filter(possible_research==TRUE) %>% 
  filter(year==2020) %>% 
  group_by(Focus.Area) %>% 
  summarise(total = format(sum(amount, na.rm = TRUE), big.mark=",", scientific=FALSE), grants = n()) %>%
  dplyr::arrange(-grants) %>%
  .kable(caption = "Open Phil (likely) research funding, 2020") %>% 
  .kable_styling()
)

By organization

(
  op_res_grants_tab_orgs_area <-
open_phil_grants %>%
    filter(possible_research==TRUE) %>%
    dplyr::group_by(Organization.Name) %>% # drop_na(!!yvar, !!treatvar) %>%
  summarise(total = sum(amount_usd_k,  na.rm = TRUE), `number of grants` = n()) %>%
      arrange(-total) %>%
    filter(total>5000) %>%
    .kable(caption = "OpenPhil (likely research) grants by corganization  and area, in $1000 USD") %>%
  .kable_styling()
)

Maybe 500 million USD per year in EA/adjacent donations (+ about 250 million from OpenPhil)

• GiveWell moving ~$80M per year

• Founders Pledge

• Longview

• Effective Giving

• EA Funds

• “Gates Foundation seems to do ~$100M-500M/yr of grants in global economic development that seem to have cost-effectiveness on par with GiveWell work”

It’s not all research funding, but some of it is, and it is all interested in prioritization/effectiveness research.

How do most ‘effective altruists’ engage; what is most strongly advocated?

2019 survey: career plans

EAs are moving towards pursuing impact through their careers.

Since 2015 “80,000 Hours thinks that only a small proportion of people should earn to give long term” (MacAskill)

There is also some support for politial influence, at least they say “the hour you spend voting is likely to be the most impactful one in your entire year on average… …influence over how hundreds of thousands or millions of dollars are spent.”

Careers: high vs less engaged

Source - EA survey 2020 forum post

Berkeley:

• Proposing ‘new ideas’ like the Poor Card

Limited audience: ‘How does this inform government policy?’, ‘How does it inform/relate to standard Economics (tractable mathematical) models of optimization?’, ‘Will this publish well’?

Considering ideas with a pre-EA policy audience.

• ‘Does one donation come at the expense of another’?

• Should an ‘efficient altruist’ purchase ‘fair trade’ products, bundling consumer choices with additional revenue to poor farmers/workers?
  

Things I care about: lining up with concepts in the discipline? But I really cared about ideas and impact.

Essex, UK:

• Experiments/trials and observational work on charitable and gift-giving: social influences, types of income/uncertainty

• Applied microeconomic theory

Moved to Exeter, UK

• to pursue ‘Impact’ (ESRC grant, REF focus)

• Building teaching/research/outreach resources, such as

  • innovationsinfundraising.org and ‘barriers to effective giving’
  • “Researching and writing for Economics students”

Why academia/Exeter?

Positives: A fairly supportive environment, research freedom, many great colleagues, moderate teaching, targets ‘deep and rigorous theoretical work’, some of the smartest people

Limitations: academic politics and poor upper-management, countervailing rewards system, constant discussion of points/games (value drift), students

Standard publications as the only way to prove value; limits collaborative and nonstandard work

UK academia rewards either ‘REF-points publications’, box-ticking accreditations, or currying favor with ‘admin’

Rethinking my priorities

In 2021 I left my secure academic post:

To pursue greater impact as a researcher at Rethink Priorities, a think tank “dedicated to figuring out the best ways to make the world a better place.” RP is closely tied to the Effective Altruism movement. My research into effective charitable giving is made possible by a grant from Pan Pan Wong under the advising of Longview Philanthropy.

To build tools and programs promoting open, collaborative, and robust research, as well as teaching, learning, and research training outside of traditional university degree schemes.

(Should you do it too? Back to this at the end)

My work at RP

0. Longview-funded: ‘Barriers to effective giving’

• Categorization and links to Economics and Psychology ‘theory’

• Meta-analysis and synthesis

• Field experiments and trials in large-scale contexts

1. EA Survey and ‘movement building’

• Survey methods: representativeness, survey design

• Identifying key questions for ‘tracking a movement and its impact’, e.g.,

  • “Which people are likely to contribute the most; ‘selecting’ where to promote’” (ML and inference)
  • “What will the impact of the EA community be in 10-20 years?” (APC modeling)
  • Do anticipated donations tend to exceed actual donations and when/why

• Analysis: Visualisation, descriptive, predictive, and

2. Outreach and message testing

• Receptiveness to parts of EA message

• Support for policies (e.g., animal welfare)

3. Future work: moral weights, measurement of satisfaction, ‘near-term’ (global health) evaluation, ‘shallow reviews’

4. Support and guidance to other RP projects (e.g., modeling the meat industry; designing behaioural trials)

5. Connection with academia; publishing, recruiting, advising students

‘Prioritization research and ethics’

GPI research agenda

E.g.,

• How to create better systems for creating and disseminating knowledge
• Whether and how to ‘discount’ future individuals (income or happiness)?
• How to make choices under moral uncertainty
• How to value ‘more happy people’ versus ‘happier people’ (population ethics)

Defining a moral framework, considering ‘what has value’, and ‘how to learn and choose’?

… how to value things and be consistent, how to use uncertainty and information in making altruistic choices

E.g.,

• moral weights

• population ethics

• moral uncertainty .

From GPI agenda:

Under what conditions would a social planner or philanthropist prioritise policies that primarily increase social welfare in the far future rather than in the near term? For instance, under what condition would such agents prioritise saving for future generations (Ramsey 1928) or reducing the risk of human extinction (Baranzini and Bourguignon 1995)?

Should one have the same levels of epistemic modesty about unusual moral views as one should about unusual empirical views?

To what extent should we be risk averse in our approach to doing good, and what are the implications of reasonable risk aversion for global prioritisation? (Quiggin 1982; Buchak 2013; Greaves et al. MS)

Social welfare criteria that are used to compare states that differ in population size typically specify a critical welfare level at which lives that are added to the population have zero contributive value to social welfare (Blackorby et al. 1995; Ng 1986; Broome 2004; Ch. 10). What kinds of lives have zero contributive value in this sense (Cockburn et al. 2014; Cowie 2017)?

Empirical measurement informing the ‘production function for good’

• ‘What has value and moral worth?’ (e.g., sentience research)

• How to measure value? (e.g., pain/pleasure DALY)

• How to achieve value?

  • Direct interventions and policies, direct/indirect, short and LT impacts
  • Very long term impacts, inference with deep uncertainties

Should we loosen migration restrictions to increase global welfare? What is a politically feasible level of migration? (from Rhys-Bernard syllabus)

Estimating, in terms of SWB, the impact of potentially highly-effective interventions, including: psychotherapy for common mental disorders; cataract surgery for blindness; deworming tablets to improve lifelong earnings (from Happier lives institute research priorities)

Empirical (behavioural) … How to get people and institutions to care about others (altruism) and about being effective in doing so?

• Barriers to considering effectiveness and acting effectively (my focus – open project HERE)

• Applied work : message testing, information and choice-architecture

Money-metric impact of a random sample of projects:

‘Multiplying the uncertain estimates of’ (Bayesian):

• Probability our research changes the “influence-target’s” choice ×
• probability this is in the right direction ×
• Funds affected ×
• Proportional increase in the funds’ impact ×
• “Counterfactual years impacted”

Divide by project cost

→ “Impact per dollar spent”

→ Extrapolate to total value of RP